Decision-focused learning has emerged as a promising approach for decision making under uncertainty by training the upstream predictive aspect of the pipeline with respect to the quality of the downstream decisions. Most existing work has focused on single stage problems. Many real-world decision problems are more appropriately modelled using multistage optimisation as contextual information such as prices or demand is revealed over time and decisions now have a bearing on future decisions. We propose decision-focused forecasting, a multiple-implicitlayer model which in its training accounts for the intertemporal decision effects of forecasts using differentiable optimisation. The recursive model reflects a fully differentiable multistage optimisation approach. We present an analysis of the gradients produced by this model showing the adjustments made to account for the state-path caused by forecasting. We demonstrate an application of the model to an energy storage arbitrage task and report that our model outperforms existing approaches.

翻译：决策导向学习已成为一种在不确定性下进行决策的有前景方法，其通过根据下游决策质量来训练流程中的上游预测环节。现有研究大多集中于单阶段问题。许多现实世界的决策问题更适合采用多阶段优化进行建模，因为价格或需求等上下文信息会随时间逐步显现，且当前决策会对未来决策产生影响。我们提出决策导向预测模型——一种多重隐层模型，该模型在训练过程中通过可微分优化技术来考量预测带来的跨期决策效应。这种递归模型体现了完全可微分的多阶段优化方法。我们对该模型产生的梯度进行了分析，展示了为适应预测导致的状态路径而进行的调整。我们将该模型应用于储能套利任务，实验结果表明我们的模型性能优于现有方法。